First session:
- Not heaps of paleo-specific R
- But building blocks to make you an expeRt
- Things that go into R (data inputs)
- How to structure your data inputs and outputs
- Getting started in R
26/06/2018
Welcome, and overview
Welcome, and overview
Second session:
- Data validation
- Data visualisation
- NMDS
- RDA
- Plotting NMDS etc for publications
- Saving and export
BD (before data): project structures
- Raw data (as entered)
- Corrected and modified data
BD (before data): project structures
Keep a record of how you went from (1) to (2) - even if you don't do it in R
- Correct/modify data in R (with reminders)
- Create a new spreadsheet and keep a .txt records
Project structures
Where should a project live?
Pros and cons of the following:
- MW-LCR shared drives
- Dropbox (C:/)
- Github (C:/)
- MW-LCR personal drive
Where should a project live
githubScreenshot
Within the project: naming files
Three principals:
- Machine readable
- Human readable
- Works with default ordering
Machine readable
- no punctuation symbols
- no spaces
- be careful with capitals
- for data, easy to parse
Machine readable
- e.g. year_site_coreNUM_type
- e.g. 2018-06-31_eweburn_X18-062_concentrations.csv
- e.g. 2018-06-31_eweburn_X18-062_age-depth.csv
- e.g. 2018-06-31_eweburn_X18-062_species-dictionary.csv
note that we separate units of metadata with a "_" and within units, with a "-".
Human readable
This applies to scripts and data
- e.g. 1_data-cleaning-vegetation.R
- e.g. 2_data-cleaning-species-dictionary.R
- e.g. function_plot-all-species.R
e.g. function_clean-italics-tilia.R
Group discussion - data & spreadsheets
example data screenshot
Booting up the R
rstudio
The basics (1)
getwd()
## [1] "/Users/oliviaburge/Documents/paleo-R-workshop/1-folders-spreadsheets-organisingData"
The basics (2)
setwd()
The basics (3)
sessionInfo()
## R version 3.5.0 (2018-04-23) ## Platform: x86_64-apple-darwin15.6.0 (64-bit) ## Running under: macOS High Sierra 10.13.5 ## ## Matrix products: default ## BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib ## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib ## ## locale: ## [1] en_NZ.UTF-8/en_NZ.UTF-8/en_NZ.UTF-8/C/en_NZ.UTF-8/en_NZ.UTF-8 ## ## attached base packages: ## [1] stats graphics grDevices utils datasets methods base ## ## other attached packages: ## [1] DiagrammeR_1.0.0 ## ## loaded via a namespace (and not attached): ## [1] Rcpp_0.12.17 highr_0.6 pillar_1.2.1 ## [4] compiler_3.5.0 RColorBrewer_1.1-2 influenceR_0.1.0 ## [7] plyr_1.8.4 bindr_0.1.1 viridis_0.5.1 ## [10] tools_3.5.0 digest_0.6.15 jsonlite_1.5 ## [13] viridisLite_0.3.0 gtable_0.2.0 evaluate_0.10.1 ## [16] tibble_1.4.2 rgexf_0.15.3 pkgconfig_2.0.1 ## [19] rlang_0.2.1 igraph_1.2.1 rstudioapi_0.7 ## [22] yaml_2.1.19 bindrcpp_0.2.2 gridExtra_2.3 ## [25] downloader_0.4 dplyr_0.7.5 stringr_1.3.1 ## [28] knitr_1.20 htmlwidgets_1.2 hms_0.4.2 ## [31] grid_3.5.0 rprojroot_1.3-2 tidyselect_0.2.4 ## [34] glue_1.2.0.9000 R6_2.2.2 Rook_1.1-1 ## [37] XML_3.98-1.11 rmarkdown_1.10 ggplot2_2.2.1.9000 ## [40] tidyr_0.8.0 purrr_0.2.5 readr_1.1.1 ## [43] magrittr_1.5 backports_1.1.2 scales_0.5.0 ## [46] htmltools_0.3.6 assertthat_0.2.0 colorspace_1.3-2 ## [49] brew_1.0-6 stringi_1.2.3 visNetwork_2.0.3 ## [52] lazyeval_0.2.1 munsell_0.4.3
The basics (4)
require(tidyverse)
## Loading required package: tidyverse
## ── Attaching packages ────────────────────────────────────────────────── tidyverse 1.2.1 ──
## ✔ ggplot2 2.2.1.9000 ✔ purrr 0.2.5 ## ✔ tibble 1.4.2 ✔ dplyr 0.7.5 ## ✔ tidyr 0.8.0 ✔ stringr 1.3.1 ## ✔ readr 1.1.1 ✔ forcats 0.3.0
## ── Conflicts ───────────────────────────────────────────────────── tidyverse_conflicts() ── ## ✖ dplyr::filter() masks stats::filter() ## ✖ dplyr::lag() masks stats::lag()
Example data
# install.packages("vegan")
# install.packages("skimr")
require(vegan)
require(skimr)
data("mite") # the data command only works for in-built data
data("mite.env") # we'll cover reading in your own data later on
Viewing data
head(mite, n = 6)
| Brachy | PHTH | HPAV | RARD | SSTR | Protopl | MEGR | MPRO | TVIE | HMIN | HMIN2 | NPRA | TVEL | ONOV | SUCT | LCIL | Oribatl1 | Ceratoz1 | PWIL | Galumna1 | Stgncrs2 | HRUF | Trhypch1 | PPEL | NCOR | SLAT | FSET | Lepidzts | Eupelops | Miniglmn | LRUG | PLAG2 | Ceratoz3 | Oppiminu | Trimalc2 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 17 | 5 | 5 | 3 | 2 | 1 | 4 | 2 | 2 | 1 | 4 | 1 | 17 | 4 | 9 | 50 | 3 | 1 | 1 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 7 | 16 | 0 | 6 | 0 | 4 | 2 | 0 | 0 | 1 | 3 | 21 | 27 | 12 | 138 | 6 | 0 | 1 | 3 | 9 | 1 | 1 | 1 | 2 | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 3 | 1 | 1 | 2 | 0 | 3 | 0 | 0 | 0 | 6 | 3 | 20 | 17 | 10 | 89 | 3 | 0 | 2 | 1 | 8 | 0 | 3 | 0 | 2 | 0 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 23 | 7 | 10 | 2 | 2 | 0 | 4 | 0 | 1 | 2 | 10 | 0 | 18 | 47 | 17 | 108 | 10 | 1 | 0 | 1 | 2 | 1 | 2 | 1 | 3 | 2 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5 | 8 | 13 | 9 | 0 | 13 | 0 | 0 | 0 | 3 | 14 | 3 | 32 | 43 | 27 | 5 | 1 | 0 | 5 | 2 | 1 | 0 | 1 | 0 | 0 | 0 | 12 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 19 | 7 | 5 | 9 | 3 | 2 | 3 | 0 | 0 | 20 | 16 | 2 | 13 | 38 | 39 | 3 | 5 | 0 | 1 | 1 | 8 | 0 | 4 | 0 | 1 | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
The basics (5)
- When you just want one column, you can use the following format
dataframeNAME$columnNAME
mite$Brachy
## [1] 17 2 4 23 5 19 17 5 3 22 36 28 3 41 6 7 9 19 12 3 5 4 19 ## [24] 4 12 6 4 9 42 20 12 4 38 5 3 3 3 8 0 1 2 0 5 0 11 4 ## [47] 0 0 10 4 2 3 3 2 1 1 0 0 1 1 6 3 19 3 4 8 4 6 20 ## [70] 5
Viewing data
skim(mite)
## Skim summary statistics ## n obs: 70 ## n variables: 35 ## ## ── Variable type:integer ────────────────────────────────────────────────────────────────── ## variable missing complete n mean sd p0 p25 p50 p75 p100 hist ## Brachy 0 70 70 8.73 10.08 0 3 4.5 11.75 42 ▇▂▁▂▁▁▁▁ ## Ceratoz1 0 70 70 1.29 1.46 0 0 1 2 5 ▇▆▁▃▁▁▁▁ ## Ceratoz3 0 70 70 1.3 2.2 0 0 0 2 9 ▇▁▁▁▁▁▁▁ ## Eupelops 0 70 70 0.64 0.99 0 0 0 1 4 ▇▃▁▁▁▁▁▁ ## FSET 0 70 70 1.86 3.18 0 0 0 2 12 ▇▂▁▁▁▁▁▁ ## Galumna1 0 70 70 0.96 1.73 0 0 0 1 8 ▇▁▁▁▁▁▁▁ ## HMIN 0 70 70 4.91 8.47 0 0 0 4.75 36 ▇▁▁▁▁▁▁▁ ## HMIN2 0 70 70 1.96 3.92 0 0 0 2.75 20 ▇▂▁▁▁▁▁▁ ## HPAV 0 70 70 8.51 7.56 0 4 6.5 12 37 ▇▇▃▃▁▁▁▁ ## HRUF 0 70 70 0.23 0.62 0 0 0 0 3 ▇▁▁▁▁▁▁▁ ## LCIL 0 70 70 35.26 88.85 0 1.25 13 44 723 ▇▁▁▁▁▁▁▁ ## Lepidzts 0 70 70 0.17 0.54 0 0 0 0 3 ▇▁▁▁▁▁▁▁ ## LRUG 0 70 70 10.43 12.66 0 0 4.5 17.75 57 ▇▂▂▁▁▁▁▁ ## MEGR 0 70 70 2.19 3.62 0 0 1 3 17 ▇▂▁▁▁▁▁▁ ## Miniglmn 0 70 70 0.24 0.79 0 0 0 0 5 ▇▁▁▁▁▁▁▁ ## MPRO 0 70 70 0.16 0.47 0 0 0 0 2 ▇▁▁▁▁▁▁▁ ## NCOR 0 70 70 1.13 1.65 0 0 0.5 1.75 7 ▇▃▂▂▁▁▁▁ ## NPRA 0 70 70 1.89 2.37 0 0 1 2.75 10 ▇▂▂▁▁▁▁▁ ## ONOV 0 70 70 17.27 18.05 0 5 10.5 24.25 73 ▇▃▂▁▁▁▁▁ ## Oppiminu 0 70 70 1.11 1.84 0 0 0 1.75 9 ▇▁▁▁▁▁▁▁ ## Oribatl1 0 70 70 1.89 3.43 0 0 0 2.75 17 ▇▁▁▁▁▁▁▁ ## PHTH 0 70 70 1.27 2.17 0 0 0 2 8 ▇▁▁▁▁▁▁▁ ## PLAG2 0 70 70 0.8 1.79 0 0 0 1 9 ▇▁▁▁▁▁▁▁ ## PPEL 0 70 70 0.17 0.54 0 0 0 0 3 ▇▁▁▁▁▁▁▁ ## Protopl 0 70 70 0.37 1.61 0 0 0 0 13 ▇▁▁▁▁▁▁▁ ## PWIL 0 70 70 1.09 1.71 0 0 0 1 8 ▇▁▁▁▁▁▁▁ ## RARD 0 70 70 1.21 2.78 0 0 0 1 13 ▇▂▁▁▁▁▁▁ ## SLAT 0 70 70 0.4 1.23 0 0 0 0 8 ▇▁▁▁▁▁▁▁ ## SSTR 0 70 70 0.31 0.97 0 0 0 0 6 ▇▁▁▁▁▁▁▁ ## Stgncrs2 0 70 70 0.73 1.83 0 0 0 0 9 ▇▁▁▁▁▁▁▁ ## SUCT 0 70 70 16.96 13.89 0 7.25 13.5 24 63 ▇▇▆▅▂▁▁▁ ## Trhypch1 0 70 70 2.61 6.14 0 0 0 2 29 ▇▁▁▁▁▁▁▁ ## Trimalc2 0 70 70 2.07 5.79 0 0 0 0 33 ▇▁▁▁▁▁▁▁ ## TVEL 0 70 70 9.06 10.93 0 0 3 19 42 ▇▁▁▂▁▁▁▁ ## TVIE 0 70 70 0.83 1.47 0 0 0 1 7 ▇▁▁▁▁▁▁▁
Live coding demo
- Select some columns
- Filter just some observations
- Histogram of one column
Select - concept
Select chooses certain columns - to keep, or to get rid of. The format is
DATANAME %>% select(col1, col2, col3)
Select - examples
mite %>% select(Brachy, PHTH, HPAV)
## Brachy PHTH HPAV ## 1 17 5 5 ## 2 2 7 16 ## 3 4 3 1 ## 4 23 7 10 ## 5 5 8 13 ## 6 19 7 5 ## 7 17 3 8 ## 8 5 4 8 ## 9 3 3 2 ## 10 22 4 5 ## 11 36 7 35 ## 12 28 2 12 ## 13 3 2 4 ## 14 41 5 12 ## 15 6 0 6 ## 16 7 2 3 ## 17 9 0 1 ## 18 19 3 7 ## 19 12 2 10 ## 20 3 1 7 ## 21 5 2 8 ## 22 4 0 4 ## 23 19 0 8 ## 24 4 0 1 ## 25 12 4 15 ## 26 6 0 4 ## 27 4 4 4 ## 28 9 0 4 ## 29 42 0 6 ## 30 20 1 2 ## 31 12 0 5 ## 32 4 0 9 ## 33 38 0 17 ## 34 5 0 14 ## 35 3 0 0 ## 36 3 1 2 ## 37 3 0 5 ## 38 8 0 6 ## 39 0 0 0 ## 40 1 0 31 ## 41 2 0 10 ## 42 0 0 12 ## 43 5 0 2 ## 44 0 0 2 ## 45 11 0 8 ## 46 4 0 4 ## 47 0 0 8 ## 48 0 0 3 ## 49 10 0 14 ## 50 4 0 37 ## 51 2 0 5 ## 52 3 0 4 ## 53 3 0 17 ## 54 2 0 7 ## 55 1 0 3 ## 56 1 0 16 ## 57 0 0 0 ## 58 0 0 12 ## 59 1 0 0 ## 60 1 0 16 ## 61 6 0 9 ## 62 3 0 5 ## 63 19 0 3 ## 64 3 0 16 ## 65 4 0 10 ## 66 8 0 18 ## 67 4 0 3 ## 68 6 0 22 ## 69 20 2 4 ## 70 5 0 11
Select - examples
names(mite)
## [1] "Brachy" "PHTH" "HPAV" "RARD" "SSTR" "Protopl" ## [7] "MEGR" "MPRO" "TVIE" "HMIN" "HMIN2" "NPRA" ## [13] "TVEL" "ONOV" "SUCT" "LCIL" "Oribatl1" "Ceratoz1" ## [19] "PWIL" "Galumna1" "Stgncrs2" "HRUF" "Trhypch1" "PPEL" ## [25] "NCOR" "SLAT" "FSET" "Lepidzts" "Eupelops" "Miniglmn" ## [31] "LRUG" "PLAG2" "Ceratoz3" "Oppiminu" "Trimalc2"
mite %>% select(-c(PHTH:Oppiminu))
## Brachy Trimalc2 ## 1 17 0 ## 2 2 0 ## 3 4 0 ## 4 23 0 ## 5 5 0 ## 6 19 0 ## 7 17 0 ## 8 5 0 ## 9 3 0 ## 10 22 0 ## 11 36 0 ## 12 28 0 ## 13 3 0 ## 14 41 0 ## 15 6 0 ## 16 7 0 ## 17 9 0 ## 18 19 0 ## 19 12 0 ## 20 3 0 ## 21 5 0 ## 22 4 0 ## 23 19 0 ## 24 4 0 ## 25 12 0 ## 26 6 0 ## 27 4 0 ## 28 9 0 ## 29 42 0 ## 30 20 0 ## 31 12 0 ## 32 4 0 ## 33 38 0 ## 34 5 0 ## 35 3 0 ## 36 3 0 ## 37 3 0 ## 38 8 0 ## 39 0 0 ## 40 1 0 ## 41 2 0 ## 42 0 0 ## 43 5 1 ## 44 0 0 ## 45 11 0 ## 46 4 0 ## 47 0 0 ## 48 0 1 ## 49 10 0 ## 50 4 0 ## 51 2 1 ## 52 3 0 ## 53 3 9 ## 54 2 1 ## 55 1 0 ## 56 1 5 ## 57 0 0 ## 58 0 0 ## 59 1 1 ## 60 1 1 ## 61 6 5 ## 62 3 0 ## 63 19 8 ## 64 3 11 ## 65 4 25 ## 66 8 9 ## 67 4 33 ## 68 6 17 ## 69 20 3 ## 70 5 14
Select - discussion
- What is the difference between
$andselect?
mite$Brachy # 'base' R mite %>% select(Brachy) # tidyverse mite %>% pull(Brachy) # we haven't discussed this one yet
What is this? %>% %>% %>%
What is this? %>% %>% %>%
Chaining allows us to write code in the order we want it done. Otherwise, it must be wrapped in brackets with the first thing to be done right in the middle.
mite.env %>% select(Shrub, Topo)
means, take the mite.env dataframe, and then select the columns Shrub and Topo.
Filter - concept
Filter selects rows in your dataframe, based on the conditions you specify. Same format as for select():
filter(DATA, CONDITION1)
- row only selected if condition satisfied
Filter - concept
filter(DATA, CONDITION1 & CONDITION2)
- both conditions must be satisfied
filter(DATA, CONDITION1 | CONDITION2)
- one or both conditions must be satisfied
Filter - example
mite.env %>% filter(WatrCont > 650)
## SubsDens WatrCont Substrate Shrub Topo ## 1 64.75 691.79 Sphagn2 Few Blanket ## 2 62.38 708.16 Barepeat Few Blanket ## 3 52.73 656.35 Sphagn1 None Blanket ## 4 52.12 826.96 Sphagn1 None Blanket
Filter - example with two conditions
Here, Shrub has to equal "Few". If you want to select two values (such as two sites) see the next slide.
mite.env %>% filter(WatrCont > 650 & Shrub == "Few")
## SubsDens WatrCont Substrate Shrub Topo ## 1 64.75 691.79 Sphagn2 Few Blanket ## 2 62.38 708.16 Barepeat Few Blanket
Filter - example selecting > 1 element
unique(mite.env$Substrate)
## [1] Sphagn1 Litter Interface Sphagn3 Sphagn4 Sphagn2 Barepeat ## Levels: Sphagn1 Sphagn2 Sphagn3 Sphagn4 Litter Barepeat Interface
mite.env %>% filter(Substrate %in% c("Litter", "Barepeat", "Interface"))
## SubsDens WatrCont Substrate Shrub Topo ## 1 54.99 434.81 Litter Few Hummock ## 2 46.07 371.72 Interface Few Hummock ## 3 80.59 266.78 Interface Many Blanket ## 4 61.43 310.70 Litter Many Blanket ## 5 37.25 239.51 Interface Many Blanket ## 6 59.93 350.64 Interface Many Blanket ## 7 35.41 321.87 Interface Few Hummock ## 8 29.56 296.95 Interface Many Hummock ## 9 44.10 383.83 Interface Many Blanket ## 10 38.61 145.68 Interface Many Hummock ## 11 32.27 291.59 Interface Many Hummock ## 12 35.30 293.49 Interface Many Blanket ## 13 32.86 323.12 Interface Many Hummock ## 14 37.33 284.27 Interface Many Blanket ## 15 53.17 367.11 Interface Many Blanket ## 16 34.76 393.62 Interface Few Blanket ## 17 47.74 528.44 Interface Few Blanket ## 18 34.26 398.20 Interface Few Blanket ## 19 26.60 386.37 Interface Few Blanket ## 20 56.65 581.00 Interface Few Blanket ## 21 62.38 708.16 Barepeat Few Blanket ## 22 46.81 538.51 Interface Few Blanket ## 23 33.98 323.96 Interface Few Blanket ## 24 28.29 434.28 Interface None Blanket ## 25 26.83 414.65 Interface None Blanket ## 26 31.98 447.65 Interface None Blanket ## 27 41.38 532.88 Interface None Blanket ## 28 56.82 613.39 Barepeat None Blanket ## 29 47.03 626.36 Interface None Blanket ## 30 48.59 634.75 Interface None Blanket ## 31 35.03 482.27 Interface None Blanket
Histogram: concept
- We use the command
ggplotwhich comes from the ggplot2 package. ggplot()just initiates the plot- then we tell it to draw a histogram with the
geom_histogram()part.
ggplot(data = DATAFRAME, aes(x = COLUMN_FOR_HISTOGRAM)) + geom_histogram()
First example plot: histogram
ggplot(data = mite.env, aes(x = SubsDens)) + geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
First example plot: histogram
ggplot(data = mite.env, aes(x = SubsDens)) + geom_histogram(binwidth = 10)
Reading in real data!
Actually, it should be real, but it should also be tidy
Reading in real data!
- Where is the file
- How do we tell R to get there
- This also depends where R has the working directory
- We need to tell R how to get from the working directory, to the file that we want to read in!
getwd()
setwd()
What happens with untidy data?
aMess <- read.csv("data/messyDataExample.csv")
head(aMess)
## Ashburton.Lakes.weight.of.vegetation.harvest.subsample X X.1 ## 1 ## 2 Date: ## 3 Lab team: ## 4 ## 5 ## 6 Wet weight ## X.2 X.3 X.4 X.5 X.6 ## 1 ## 2 ## 3 ## 4 ## 5 ## 6 Wet weight 1 Wet weight 2 Wet weight 3 Average wet weight (g) ## X.7 X.8 X.9 X.10 ## 1 ## 2 ## 3 ## 4 ## 5 ## 6 Dry weight Dry weight 1 Dry weight 2 Dry weight 3 ## X.11 X.12 X.13 X.14 X.15 X.16 X.17 X.18 X.19 X.20 X.21 ## 1 NA ## 2 NA ## 3 NA ## 4 NA ## 5 NA ## 6 Average dry weight (g) NA ## X.22 X.23 X.24 X.25 X.26 X.27 X.28 X.29 X.30 X.31 X.32 ## 1 NA ## 2 NA ## 3 NA ## 4 NA ## 5 NA ## 6 NA
What happens with untidy data?
To see the whole thing:
View(aMess)Compare the output of
names(aMess)andnames(mite.env)
Let's fix it, and read it back in
[group task]
Read in tidied data
- Where is it
- Where is the working directory?
- Where is the file in relation to our working directory?
What did we call it (ie the filename)?!
Then we can read it back in
Read in Awarua data
- Where is it?
- Where is the working directory?
- Where is the file in relation to our working directory?
- What is the filename?
Awarua data
awarua <- read.csv("data/awaruaForestExample.csv")
head(awarua)
## Depth..cm. DACCUP DACDAC FUSTYP PRUTAX LOPMEN PODOCA PHYLLO HALOCA ## 1 1 66 12 2 4 0 5 2 0 ## 2 3 47 6 3 12 4 9 13 0 ## 3 5 70 3 1 8 2 11 18 0 ## 4 7 100 9 5 18 0 10 18 0 ## 5 9 95 11 8 8 0 8 29 0 ## 6 11 87 16 4 18 1 5 27 0 ## ELAEOC HOHERI WEINMA ASTERA COPROS MYRSIN DRATYP GAULTH RUBUS MUEHLE ## 1 6 0 56 0 4 9 0 0 0 0 ## 2 24 1 66 3 7 16 1 1 1 1 ## 3 13 0 35 0 4 22 0 0 0 0 ## 4 14 0 37 3 11 23 0 2 0 0 ## 5 9 0 50 2 8 17 1 0 1 0 ## 6 12 0 17 0 18 21 0 0 0 2 ## PENNAN PSEUDO ARALIA POACEA LEPTYP GONTYP EMPTYP GLEICH MYRTYP PHORMI ## 1 0 0 1 74 12 0 0 1 0 0 ## 2 0 4 5 27 25 0 0 0 10 2 ## 3 3 1 0 13 47 0 0 1 13 0 ## 4 0 1 0 14 55 0 1 0 44 1 ## 5 0 0 4 9 82 0 1 4 30 1 ## 6 1 1 1 4 82 0 0 3 26 0 ## RANTYP SPHAGN GUNNER NERTER CAREX BAUTYP SCHTYP PTEESC CYDTYP DICSQU ## 1 0 0 0 0 0 1 0 3 2 0 ## 2 2 0 0 0 0 4 0 6 2 0 ## 3 0 0 0 0 0 1 0 4 1 0 ## 4 0 0 0 0 0 2 0 8 2 1 ## 5 0 0 0 0 2 2 0 3 0 0 ## 6 1 0 0 0 2 1 0 1 0 0 ## MONOL HISINC MICPUS LYCLAT LYCFAS PINACE KIRTYP RUMEX PCHARS PCHARL ## 1 3 2 2 0 0 8 0 0 4 0 ## 2 2 0 2 0 0 0 1 0 5 0 ## 3 3 1 0 1 0 1 3 0 22 0 ## 4 7 0 2 0 0 0 1 0 10 0 ## 5 5 0 0 0 0 0 0 1 26 0 ## 6 5 1 3 0 0 0 0 3 45 0 ## BRYOPH NEOPTP PITTOS ## 1 0 0 0 ## 2 0 0 0 ## 3 1 0 0 ## 4 0 0 0 ## 5 0 0 0 ## 6 0 0 0
Awarua - exercise
- create a subset of the data.frame called "awaruaCharcoal"
- we only want up to and including 20 cm
- we only want the Depth column, and the two charcoal columns
- bonus rename the depth column
Depth..cm.to depth_CM
awaruaCharcoal <- awarua %>% [...]
Awarua - exercise answer
awaruaCharcoal <- awarua %>% filter(Depth..cm. <= 20) %>% select(depth_CM = Depth..cm., PCHARS, PCHARL) head(awaruaCharcoal, 2)
## depth_CM PCHARS PCHARL ## 1 1 4 0 ## 2 3 5 0
Awarua - plot example
charcoalPlot <- ggplot(data = awaruaCharcoal, aes(x = depth_CM, y = PCHARS)) +
geom_line() +
theme_classic() +
labs(x = "Depth (cm)", y = "Charcoal", title = "Charcoal within Awarua wetland",
subtitle = "Forest site")
charcoalPlot
Awarua - plot exercise - modifying plots
- Can you change the title of the y-axis to something else?
- Can you change the colour of the line?
- [extension] try googling to see if you can find out how to reverse the direction of the x-axis
Awarua - plot exercise - creating a plot
- Take the original awarua data (
awarua), make a plot of depth vs DACCUP - Style it as you want
- How would we add extra lines for DACDAC & MYRSIN?
Awarua - plot creation answer
- NB there are easier ways of doing this - but we have to reshape the data first
awaruaPlot <- ggplot(awarua, aes(x = Depth..cm., y = DACCUP)) + geom_line() + geom_line(aes(y = DACDAC), linetype = "dashed") + geom_line(aes(y = MYRSIN), linetype = "dotted") + theme_classic() + scale_x_reverse() + coord_flip() + # switches the x and y axes labs(y = "Count", x = "Depth (cm)") awaruaPlot
Mutate - concept
- Sometimes we want to change values in a column. For example, take the log or square-root.
- Or we may want to calculate the percentage instead of counts
- When we change values, we use the function
mutate. - We give the name of the new column we want, and what we want to do to the old column
dataFRAME %>% mutate(logDACDAC = log(DACDAC))
dataFRAME %>% mutate(sqrtCHARCOAL_SML = sqrt(PCHARS))
Mutate - example
awaruaCharcoal %>% mutate(totalCharcoal = PCHARS + PCHARL)
## depth_CM PCHARS PCHARL totalCharcoal ## 1 1 4 0 4 ## 2 3 5 0 5 ## 3 5 22 0 22 ## 4 7 10 0 10 ## 5 9 26 0 26 ## 6 11 45 0 45 ## 7 13 26 0 26 ## 8 15 17 1 18 ## 9 17 5 2 7 ## 10 19 0 0 0
Mutate - example
# just calculate rowSums
rowSums(awarua)
# but we don't want to sum the charcoal and depth columns!
rowSums(awarua %>% select(-c(Depth..cm., PCHARL, PCHARS)))
# also we'd like it actually as a new column in the dataframe...
# so we take the code above, give the new col a name ("allRows")
awaruaWithTotals <- awarua %>%
mutate(allRows = rowSums(awarua %>% select(-c(Depth..cm., PCHARL, PCHARS))))
Mutate - example
- to transform everything to percents,
- we use a special form of mutate
- and we choose which columns to
awaruaVeg <- awaruaWithTotals %>%
select(-c(PCHARL, PCHARS, allRows, Depth..cm.)) %>%
mutate_all(.funs = function(eachColumn) {100 * eachColumn / awaruaWithTotals$allRows})
head(awaruaVeg)
## DACCUP DACDAC FUSTYP PRUTAX LOPMEN PODOCA PHYLLO HALOCA ## 1 24.00000 4.363636 0.7272727 1.454545 0.0000000 1.818182 0.7272727 0 ## 2 15.30945 1.954397 0.9771987 3.908795 1.3029316 2.931596 4.2345277 0 ## 3 24.91103 1.067616 0.3558719 2.846975 0.7117438 3.914591 6.4056940 0 ## 4 25.70694 2.313625 1.2853470 4.627249 0.0000000 2.570694 4.6272494 0 ## 5 24.29668 2.813299 2.0460358 2.046036 0.0000000 2.046036 7.4168798 0 ## 6 23.96694 4.407713 1.1019284 4.958678 0.2754821 1.377410 7.4380165 0 ## ELAEOC HOHERI WEINMA ASTERA COPROS MYRSIN DRATYP ## 1 2.181818 0.0000000 20.363636 0.0000000 1.454545 3.272727 0.0000000 ## 2 7.817590 0.3257329 21.498371 0.9771987 2.280130 5.211726 0.3257329 ## 3 4.626335 0.0000000 12.455516 0.0000000 1.423488 7.829181 0.0000000 ## 4 3.598972 0.0000000 9.511568 0.7712082 2.827763 5.912596 0.0000000 ## 5 2.301790 0.0000000 12.787724 0.5115090 2.046036 4.347826 0.2557545 ## 6 3.305785 0.0000000 4.683196 0.0000000 4.958678 5.785124 0.0000000 ## GAULTH RUBUS MUEHLE PENNAN PSEUDO ARALIA POACEA ## 1 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.3636364 26.909091 ## 2 0.3257329 0.3257329 0.3257329 0.0000000 1.3029316 1.6286645 8.794788 ## 3 0.0000000 0.0000000 0.0000000 1.0676157 0.3558719 0.0000000 4.626335 ## 4 0.5141388 0.0000000 0.0000000 0.0000000 0.2570694 0.0000000 3.598972 ## 5 0.0000000 0.2557545 0.0000000 0.0000000 0.0000000 1.0230179 2.301790 ## 6 0.0000000 0.0000000 0.5509642 0.2754821 0.2754821 0.2754821 1.101928 ## LEPTYP GONTYP EMPTYP GLEICH MYRTYP PHORMI RANTYP ## 1 4.363636 0 0.0000000 0.3636364 0.000000 0.0000000 0.0000000 ## 2 8.143322 0 0.0000000 0.0000000 3.257329 0.6514658 0.6514658 ## 3 16.725979 0 0.0000000 0.3558719 4.626335 0.0000000 0.0000000 ## 4 14.138817 0 0.2570694 0.0000000 11.311054 0.2570694 0.0000000 ## 5 20.971867 0 0.2557545 1.0230179 7.672634 0.2557545 0.0000000 ## 6 22.589532 0 0.0000000 0.8264463 7.162534 0.0000000 0.2754821 ## SPHAGN GUNNER NERTER CAREX BAUTYP SCHTYP PTEESC CYDTYP ## 1 0 0 0 0.0000000 0.3636364 0 1.0909091 0.7272727 ## 2 0 0 0 0.0000000 1.3029316 0 1.9543974 0.6514658 ## 3 0 0 0 0.0000000 0.3558719 0 1.4234875 0.3558719 ## 4 0 0 0 0.0000000 0.5141388 0 2.0565553 0.5141388 ## 5 0 0 0 0.5115090 0.5115090 0 0.7672634 0.0000000 ## 6 0 0 0 0.5509642 0.2754821 0 0.2754821 0.0000000 ## DICSQU MONOL HISINC MICPUS LYCLAT LYCFAS PINACE ## 1 0.0000000 1.0909091 0.7272727 0.7272727 0.0000000 0 2.9090909 ## 2 0.0000000 0.6514658 0.0000000 0.6514658 0.0000000 0 0.0000000 ## 3 0.0000000 1.0676157 0.3558719 0.0000000 0.3558719 0 0.3558719 ## 4 0.2570694 1.7994859 0.0000000 0.5141388 0.0000000 0 0.0000000 ## 5 0.0000000 1.2787724 0.0000000 0.0000000 0.0000000 0 0.0000000 ## 6 0.0000000 1.3774105 0.2754821 0.8264463 0.0000000 0 0.0000000 ## KIRTYP RUMEX BRYOPH NEOPTP PITTOS ## 1 0.0000000 0.0000000 0.0000000 0 0 ## 2 0.3257329 0.0000000 0.0000000 0 0 ## 3 1.0676157 0.0000000 0.3558719 0 0 ## 4 0.2570694 0.0000000 0.0000000 0 0 ## 5 0.0000000 0.2557545 0.0000000 0 0 ## 6 0.0000000 0.8264463 0.0000000 0 0
Mutate example - cont
- Now we put the Depths column back with the vegetation
awaruaVegDepths <- data.frame(Depths = awaruaWithTotals$Depth..cm.,
awaruaVeg)
head(awaruaVegDepths)
## Depths DACCUP DACDAC FUSTYP PRUTAX LOPMEN PODOCA PHYLLO ## 1 1 24.00000 4.363636 0.7272727 1.454545 0.0000000 1.818182 0.7272727 ## 2 3 15.30945 1.954397 0.9771987 3.908795 1.3029316 2.931596 4.2345277 ## 3 5 24.91103 1.067616 0.3558719 2.846975 0.7117438 3.914591 6.4056940 ## 4 7 25.70694 2.313625 1.2853470 4.627249 0.0000000 2.570694 4.6272494 ## 5 9 24.29668 2.813299 2.0460358 2.046036 0.0000000 2.046036 7.4168798 ## 6 11 23.96694 4.407713 1.1019284 4.958678 0.2754821 1.377410 7.4380165 ## HALOCA ELAEOC HOHERI WEINMA ASTERA COPROS MYRSIN ## 1 0 2.181818 0.0000000 20.363636 0.0000000 1.454545 3.272727 ## 2 0 7.817590 0.3257329 21.498371 0.9771987 2.280130 5.211726 ## 3 0 4.626335 0.0000000 12.455516 0.0000000 1.423488 7.829181 ## 4 0 3.598972 0.0000000 9.511568 0.7712082 2.827763 5.912596 ## 5 0 2.301790 0.0000000 12.787724 0.5115090 2.046036 4.347826 ## 6 0 3.305785 0.0000000 4.683196 0.0000000 4.958678 5.785124 ## DRATYP GAULTH RUBUS MUEHLE PENNAN PSEUDO ARALIA ## 1 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.3636364 ## 2 0.3257329 0.3257329 0.3257329 0.3257329 0.0000000 1.3029316 1.6286645 ## 3 0.0000000 0.0000000 0.0000000 0.0000000 1.0676157 0.3558719 0.0000000 ## 4 0.0000000 0.5141388 0.0000000 0.0000000 0.0000000 0.2570694 0.0000000 ## 5 0.2557545 0.0000000 0.2557545 0.0000000 0.0000000 0.0000000 1.0230179 ## 6 0.0000000 0.0000000 0.0000000 0.5509642 0.2754821 0.2754821 0.2754821 ## POACEA LEPTYP GONTYP EMPTYP GLEICH MYRTYP PHORMI ## 1 26.909091 4.363636 0 0.0000000 0.3636364 0.000000 0.0000000 ## 2 8.794788 8.143322 0 0.0000000 0.0000000 3.257329 0.6514658 ## 3 4.626335 16.725979 0 0.0000000 0.3558719 4.626335 0.0000000 ## 4 3.598972 14.138817 0 0.2570694 0.0000000 11.311054 0.2570694 ## 5 2.301790 20.971867 0 0.2557545 1.0230179 7.672634 0.2557545 ## 6 1.101928 22.589532 0 0.0000000 0.8264463 7.162534 0.0000000 ## RANTYP SPHAGN GUNNER NERTER CAREX BAUTYP SCHTYP PTEESC ## 1 0.0000000 0 0 0 0.0000000 0.3636364 0 1.0909091 ## 2 0.6514658 0 0 0 0.0000000 1.3029316 0 1.9543974 ## 3 0.0000000 0 0 0 0.0000000 0.3558719 0 1.4234875 ## 4 0.0000000 0 0 0 0.0000000 0.5141388 0 2.0565553 ## 5 0.0000000 0 0 0 0.5115090 0.5115090 0 0.7672634 ## 6 0.2754821 0 0 0 0.5509642 0.2754821 0 0.2754821 ## CYDTYP DICSQU MONOL HISINC MICPUS LYCLAT LYCFAS ## 1 0.7272727 0.0000000 1.0909091 0.7272727 0.7272727 0.0000000 0 ## 2 0.6514658 0.0000000 0.6514658 0.0000000 0.6514658 0.0000000 0 ## 3 0.3558719 0.0000000 1.0676157 0.3558719 0.0000000 0.3558719 0 ## 4 0.5141388 0.2570694 1.7994859 0.0000000 0.5141388 0.0000000 0 ## 5 0.0000000 0.0000000 1.2787724 0.0000000 0.0000000 0.0000000 0 ## 6 0.0000000 0.0000000 1.3774105 0.2754821 0.8264463 0.0000000 0 ## PINACE KIRTYP RUMEX BRYOPH NEOPTP PITTOS ## 1 2.9090909 0.0000000 0.0000000 0.0000000 0 0 ## 2 0.0000000 0.3257329 0.0000000 0.0000000 0 0 ## 3 0.3558719 1.0676157 0.0000000 0.3558719 0 0 ## 4 0.0000000 0.2570694 0.0000000 0.0000000 0 0 ## 5 0.0000000 0.0000000 0.2557545 0.0000000 0 0 ## 6 0.0000000 0.0000000 0.8264463 0.0000000 0 0